Warm-started wavefront reconstruction for adaptive optics

Laurent Lessard, Matthew West, Douglas MacMynowski, Sanjay Lall

Research output: Contribution to journalArticle

Abstract

Future extreme adaptive optics (ExAO) systems have been suggested with up to 105 sensors and actuators. We analyze the computational speed of iterative reconstruction algorithms for such large systems. We compare a total of 15 different scalable methods, including multigrid, preconditioned conjugate-gradient, and several newvariants of these. Simulations on a 128×128 square sensor/actuator geometry using Taylor frozen-flow dynamics are carried out using both open-loop and closed-loop measurements, and algorithms are compared on a basis of the mean squared error and floating-point multiplications required. We also investigate the use of warm starting, where the most recent estimate is used to initialize the iterative scheme. In open-loop estimation or pseudo-open-loop control, warm starting provides a significant computational speedup; almost every algorithm tested converges in one iteration. In a standard closed-loop implementation, using a single iteration per time step, most algorithms give the minimum error even in cold start, and every algorithm gives the minimum error if warm started. The best algorithm is therefore the one with the smallest computational cost per iteration, not necessarily the one with the best quasi-static performance.

Original languageEnglish (US)
Pages (from-to)1147-1155
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume25
Issue number5
DOIs
StatePublished - May 1 2008

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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